I am using the ABOD method to identify multi-dimensional outliers in my data. Everything is done in R as I am not familiar with other languages for such purposes.
I use a function that calculates the outlier degree and gives me a vector that contains the scores of all my observations. In theory any observation with a low ABOD is an outlier and any observation with a high ABOD is not an outlier.
I am struggling with determining the interval that I should use as my filter. Do you have any suggestions?
Note: I have thought about using a robust version of Tukey's method for detecting outliers (i will apply it on the ABOD scores and see if there are any extremes according to it). Is that a sensible solution to my problem?
My question is strictly theoretical: I do not want a practical solution for it as CV is not the place for that. I only want to check if I am doing something sensible and if it can have useful results.